Textual Analysis using Python for HSS - Using LLMs

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COURSE DESCRIPTION
This is the fourth part of the four-part series for humanities and social sciences researchers (HSS) and librarians. 

Modern Text Analysis with Python explores the evolution of linguistic computation, moving beyond static rules of conventional natural language processing (NLP) techniques toward the era of Large Language Models (LLMs). Participants will navigate the shift from simple word representations to sophisticated context-aware embeddings, exploring practical applications of LLMs such as automated summarization, sentence completion, and advanced sentiment analysis using popular language models like GPT, BART and BERT. The session concludes with hands-on insights into state-of-the-art models like Gemini, Claude, and GPT-5, focusing on how to integrate these powerhouses into workflows via application programming interface (API).

This is a continuation of the HSS Python and TextBlob Series. It is highly recommended that you complete the 2-part Python series, Introduction to Python & Coding for HSS - Part I & II in the HSS Python Series, as well as Introduction to Text Analysis with Python using TextBlob before registering for this session. 

You do not need any previous knowledge of the tools that will be presented.

You need a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc) on which you have administrative privileges, as you may need to pre-load specific software packages.

COURSE EXPECTATIONS
  • See instructions for how to download and setup Python here.

Meet your teaching team

Yashar Monfared

Instructor

Digital Training Specialist
Ph.D. Electrical and Computer Engineering, Dalhousie University

Yashar joined ACENET in 2023 and is based in Nova Scotia. With a Ph.D. from Dalhousie University in Electrical and Computer Engineering, he has extensive experience managing research projects, developing curriculum, and teaching in various disciplines. His research focused on optical systems, nanomaterials, and their applications in various fields. Yashar has secured academic grants, published over 40 research articles, and instructed courses at multiple universities.

Meghan Landry

Helper

Manager, Client Engagement and Support | Humanities & Social Sciences Research Specialist
Meghan Landry is the Humanities & Social Sciences (HSS) Research Specialist with ACENET, and one of the Alliance HSS National Team Leads. She possesses an MLIS from McGill University and a BA in English Literature from UPEI. She joined ACENET from St. Francis Xavier University where she was the Scholarly Communications Librarian. Meghan specializes in working with sensitive data, digital humanities, and research data management. She is still based at StFX University, but serves all of Atlantic Canada and is active in national and regional humanities & social sciences initiatives.

ACENET
We are an Atlantic Canadian not-for-profit providing access to advanced digital research infrastructure, expertise, support and training to academic, industry and government researchers. Learn more about ACENET

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